Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations724
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.5 KiB
Average record size in memory104.0 B

Variable types

DateTime1
Numeric11

Alerts

Ex Rate (sight bill) is highly overall correlated with Thai Hom Mali Rice Grade B and 2 other fieldsHigh correlation
Pakistan_White_5% is highly overall correlated with Parboiled Rice 100% and 3 other fieldsHigh correlation
Parboiled Rice 100% is highly overall correlated with Pakistan_White_5% and 3 other fieldsHigh correlation
Thai Hom Mali Rice Grade B is highly overall correlated with Ex Rate (sight bill)High correlation
Vietnam_White_5% is highly overall correlated with Pakistan_White_5% and 3 other fieldsHigh correlation
White Broken Rice A.1 Super is highly overall correlated with Ex Rate (sight bill) and 4 other fieldsHigh correlation
White Glutinous Rice 10% is highly overall correlated with Ex Rate (sight bill)High correlation
White Rice 5% is highly overall correlated with Pakistan_White_5% and 3 other fieldsHigh correlation
Date has unique valuesUnique

Reproduction

Analysis started2024-11-11 07:27:36.433752
Analysis finished2024-11-11 07:27:51.771730
Duration15.34 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Date
Date

UNIQUE 

Distinct724
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
Minimum2008-01-09 00:00:00
Maximum2022-12-21 00:00:00
2024-11-11T15:27:52.109362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:52.291933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ex Rate (sight bill)
Real number (ℝ)

HIGH CORRELATION 

Distinct362
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.416561
Minimum28.75
Maximum37.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:52.454311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum28.75
5-th percentile29.9
Q130.94
median32.27
Q333.7425
95-th percentile35.5585
Maximum37.92
Range9.17
Interquartile range (IQR)2.8025

Descriptive statistics

Standard deviation1.8220577
Coefficient of variation (CV)0.056207619
Kurtosis-0.49935579
Mean32.416561
Median Absolute Deviation (MAD)1.345
Skewness0.46342086
Sum23469.59
Variance3.3198942
MonotonicityNot monotonic
2024-11-11T15:27:52.600150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.6 17
 
2.3%
29.7 11
 
1.5%
33 10
 
1.4%
29.9 10
 
1.4%
33.9 9
 
1.2%
30.9 9
 
1.2%
31.4 9
 
1.2%
31.1 8
 
1.1%
30.65 7
 
1.0%
32.3 7
 
1.0%
Other values (352) 627
86.6%
ValueCountFrequency (%)
28.75 1
 
0.1%
28.9 1
 
0.1%
29.15 1
 
0.1%
29.2 1
 
0.1%
29.25 1
 
0.1%
29.3 1
 
0.1%
29.5 2
 
0.3%
29.6 3
 
0.4%
29.67 1
 
0.1%
29.7 11
1.5%
ValueCountFrequency (%)
37.92 1
0.1%
37.91 1
0.1%
37.83 1
0.1%
37.76 1
0.1%
37.39 1
0.1%
37.02 1
0.1%
36.91 1
0.1%
36.61 2
0.3%
36.55 1
0.1%
36.4 2
0.3%

Thai Hom Mali Rice Grade B
Real number (ℝ)

HIGH CORRELATION 

Distinct389
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean967.21685
Minimum632
Maximum1263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:52.738439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum632
5-th percentile668.15
Q1855.75
median982
Q31110
95-th percentile1180
Maximum1263
Range631
Interquartile range (IQR)254.25

Descriptive statistics

Standard deviation159.56896
Coefficient of variation (CV)0.16497744
Kurtosis-0.89594412
Mean967.21685
Median Absolute Deviation (MAD)128
Skewness-0.35373891
Sum700265
Variance25462.253
MonotonicityNot monotonic
2024-11-11T15:27:52.878217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1127 9
 
1.2%
1180 8
 
1.1%
1113 7
 
1.0%
1112 7
 
1.0%
1081 7
 
1.0%
1139 7
 
1.0%
963 6
 
0.8%
979 6
 
0.8%
1043 6
 
0.8%
1067 6
 
0.8%
Other values (379) 655
90.5%
ValueCountFrequency (%)
632 1
 
0.1%
635 1
 
0.1%
638 2
0.3%
642 1
 
0.1%
644 1
 
0.1%
647 4
0.6%
648 1
 
0.1%
651 2
0.3%
652 3
0.4%
653 1
 
0.1%
ValueCountFrequency (%)
1263 1
0.1%
1261 1
0.1%
1254 1
0.1%
1252 1
0.1%
1251 1
0.1%
1247 1
0.1%
1240 1
0.1%
1238 1
0.1%
1236 1
0.1%
1234 1
0.1%

White Rice 5%
Real number (ℝ)

HIGH CORRELATION 

Distinct256
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean481.27072
Minimum350
Maximum1022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:53.026645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile374
Q1410
median447
Q3542.25
95-th percentile612.85
Maximum1022
Range672
Interquartile range (IQR)132.25

Descriptive statistics

Standard deviation99.700804
Coefficient of variation (CV)0.20716158
Kurtosis4.8982525
Mean481.27072
Median Absolute Deviation (MAD)51.5
Skewness1.7949446
Sum348440
Variance9940.2503
MonotonicityNot monotonic
2024-11-11T15:27:53.222551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
403 11
 
1.5%
410 11
 
1.5%
441 10
 
1.4%
549 10
 
1.4%
420 10
 
1.4%
402 9
 
1.2%
404 8
 
1.1%
432 7
 
1.0%
493 7
 
1.0%
564 7
 
1.0%
Other values (246) 634
87.6%
ValueCountFrequency (%)
350 1
 
0.1%
352 1
 
0.1%
354 1
 
0.1%
356 2
0.3%
360 3
0.4%
363 1
 
0.1%
364 2
0.3%
365 2
0.3%
366 4
0.6%
367 1
 
0.1%
ValueCountFrequency (%)
1022 1
0.1%
1003 1
0.1%
944 1
0.1%
923 1
0.1%
922 1
0.1%
878 1
0.1%
864 1
0.1%
857 1
0.1%
856 1
0.1%
846 1
0.1%

White Broken Rice A.1 Super
Real number (ℝ)

HIGH CORRELATION 

Distinct228
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403.5221
Minimum295
Maximum801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:53.375419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum295
5-th percentile312
Q1338
median382
Q3435.25
95-th percentile555.55
Maximum801
Range506
Interquartile range (IQR)97.25

Descriptive statistics

Standard deviation84.23338
Coefficient of variation (CV)0.20874539
Kurtosis2.7667697
Mean403.5221
Median Absolute Deviation (MAD)46.5
Skewness1.4454391
Sum292150
Variance7095.2623
MonotonicityNot monotonic
2024-11-11T15:27:53.536021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 11
 
1.5%
331 10
 
1.4%
332 10
 
1.4%
310 10
 
1.4%
387 9
 
1.2%
345 9
 
1.2%
424 8
 
1.1%
359 8
 
1.1%
375 8
 
1.1%
329 8
 
1.1%
Other values (218) 633
87.4%
ValueCountFrequency (%)
295 1
 
0.1%
297 2
0.3%
298 2
0.3%
299 2
0.3%
300 1
 
0.1%
301 1
 
0.1%
305 4
0.6%
306 1
 
0.1%
307 1
 
0.1%
309 3
0.4%
ValueCountFrequency (%)
801 1
0.1%
800 1
0.1%
796 1
0.1%
781 1
0.1%
766 1
0.1%
755 1
0.1%
727 1
0.1%
677 1
0.1%
668 1
0.1%
639 2
0.3%

White Glutinous Rice 10%
Real number (ℝ)

HIGH CORRELATION 

Distinct417
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean810.64088
Minimum441
Maximum1537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:53.684565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum441
5-th percentile495.75
Q1693.75
median792.5
Q3917.5
95-th percentile1137.25
Maximum1537
Range1096
Interquartile range (IQR)223.75

Descriptive statistics

Standard deviation193.36278
Coefficient of variation (CV)0.23853075
Kurtosis1.042259
Mean810.64088
Median Absolute Deviation (MAD)109.5
Skewness0.68508321
Sum586904
Variance37389.165
MonotonicityNot monotonic
2024-11-11T15:27:54.049480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
792 9
 
1.2%
958 8
 
1.1%
919 8
 
1.1%
675 7
 
1.0%
709 7
 
1.0%
470 7
 
1.0%
805 7
 
1.0%
857 6
 
0.8%
789 6
 
0.8%
777 6
 
0.8%
Other values (407) 653
90.2%
ValueCountFrequency (%)
441 1
 
0.1%
443 2
 
0.3%
457 1
 
0.1%
462 1
 
0.1%
464 1
 
0.1%
465 2
 
0.3%
466 1
 
0.1%
467 1
 
0.1%
469 5
0.7%
470 7
1.0%
ValueCountFrequency (%)
1537 1
0.1%
1533 1
0.1%
1527 1
0.1%
1471 1
0.1%
1448 1
0.1%
1447 1
0.1%
1429 1
0.1%
1386 1
0.1%
1380 1
0.1%
1374 1
0.1%

Parboiled Rice 100%
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.02901
Minimum359
Maximum1080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:54.190704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum359
5-th percentile380
Q1419
median463.5
Q3563
95-th percentile658.55
Maximum1080
Range721
Interquartile range (IQR)144

Descriptive statistics

Standard deviation111.71793
Coefficient of variation (CV)0.22387062
Kurtosis5.4611581
Mean499.02901
Median Absolute Deviation (MAD)60.5
Skewness1.8697706
Sum361297
Variance12480.897
MonotonicityNot monotonic
2024-11-11T15:27:54.339091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
420 12
 
1.7%
421 10
 
1.4%
415 8
 
1.1%
439 8
 
1.1%
406 8
 
1.1%
419 8
 
1.1%
431 7
 
1.0%
405 7
 
1.0%
410 7
 
1.0%
423 7
 
1.0%
Other values (266) 642
88.7%
ValueCountFrequency (%)
359 1
 
0.1%
361 1
 
0.1%
362 1
 
0.1%
363 2
0.3%
364 2
0.3%
365 1
 
0.1%
367 1
 
0.1%
368 2
0.3%
371 2
0.3%
372 4
0.6%
ValueCountFrequency (%)
1080 1
0.1%
1078 1
0.1%
1074 1
0.1%
1054 1
0.1%
996 1
0.1%
990 1
0.1%
948 1
0.1%
941 1
0.1%
939 1
0.1%
928 1
0.1%

Estimated Temperature
Real number (ℝ)

Distinct168
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.005652
Minimum23.39133
Maximum30.026318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:54.483220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum23.39133
5-th percentile23.952295
Q125.864854
median27.324126
Q328.145904
95-th percentile29.381883
Maximum30.026318
Range6.6349874
Interquartile range (IQR)2.2810502

Descriptive statistics

Standard deviation1.6503295
Coefficient of variation (CV)0.061110523
Kurtosis-0.6164439
Mean27.005652
Median Absolute Deviation (MAD)1.1257388
Skewness-0.4550283
Sum19552.092
Variance2.7235875
MonotonicityNot monotonic
2024-11-11T15:27:54.630530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.12967135 9
 
1.2%
27.65883996 9
 
1.2%
26.71717717 9
 
1.2%
27.18800857 9
 
1.2%
25.84563821 8
 
1.1%
27.97940601 8
 
1.1%
29.38188251 8
 
1.1%
23.95229494 8
 
1.1%
28.82089191 8
 
1.1%
27.37834466 8
 
1.1%
Other values (158) 640
88.4%
ValueCountFrequency (%)
23.39133042 4
0.6%
23.40913881 4
0.6%
23.57720883 4
0.6%
23.59515874 4
0.6%
23.75640257 4
0.6%
23.84544456 3
 
0.4%
23.86325296 3
 
0.4%
23.94518201 5
0.7%
23.95229494 8
1.1%
23.97010334 4
0.6%
ValueCountFrequency (%)
30.02631785 4
0.6%
29.94985942 5
0.7%
29.93893678 3
 
0.4%
29.84063309 4
0.6%
29.81878782 4
0.6%
29.66587096 4
0.6%
29.453023 3
 
0.4%
29.4146504 4
0.6%
29.40372777 4
0.6%
29.38188251 8
1.1%

Estimated Precipitation
Real number (ℝ)

Distinct180
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1714.8669
Minimum164.4692
Maximum4213.6569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:54.781303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum164.4692
5-th percentile205.98934
Q1550.24345
median1818.8184
Q32717.6678
95-th percentile3461.6317
Maximum4213.6569
Range4049.1877
Interquartile range (IQR)2167.4244

Descriptive statistics

Standard deviation1181.5449
Coefficient of variation (CV)0.68900096
Kurtosis-1.3395258
Mean1714.8669
Median Absolute Deviation (MAD)1131.6548
Skewness0.20813056
Sum1241563.6
Variance1396048.4
MonotonicityNot monotonic
2024-11-11T15:27:54.941325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2356.741434 5
 
0.7%
3208.746855 5
 
0.7%
3219.515819 5
 
0.7%
466.7210969 5
 
0.7%
1038.026249 5
 
0.7%
3152.38538 5
 
0.7%
1546.560006 5
 
0.7%
2668.110406 5
 
0.7%
193.422565 5
 
0.7%
2203.701598 5
 
0.7%
Other values (170) 674
93.1%
ValueCountFrequency (%)
164.4691986 3
0.4%
178.5103558 3
0.4%
185.0275342 4
0.6%
193.422565 5
0.7%
193.7556512 4
0.6%
197.795422 5
0.7%
201.1426247 4
0.6%
205.0189947 4
0.6%
205.848568 4
0.6%
205.9893441 4
0.6%
ValueCountFrequency (%)
4213.656944 4
0.6%
4062.399848 5
0.7%
4058.056252 4
0.6%
4048.024076 5
0.7%
3912.384734 4
0.6%
3902.712681 4
0.6%
3590.520115 5
0.7%
3492.482096 4
0.6%
3461.631681 4
0.6%
3429.165736 4
0.6%

Vietnam_White_5%
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean426.61625
Minimum325
Maximum1075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:55.092345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum325
5-th percentile344
Q1375.75
median408.5
Q3456
95-th percentile564.35
Maximum1075
Range750
Interquartile range (IQR)80.25

Descriptive statistics

Standard deviation89.753168
Coefficient of variation (CV)0.21038384
Kurtosis19.353585
Mean426.61625
Median Absolute Deviation (MAD)39.5
Skewness3.6063051
Sum308870.17
Variance8055.6312
MonotonicityNot monotonic
2024-11-11T15:27:55.260151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350 22
 
3.0%
396 21
 
2.9%
405 16
 
2.2%
470 14
 
1.9%
465 14
 
1.9%
353 13
 
1.8%
415 13
 
1.8%
419 12
 
1.7%
456 12
 
1.7%
399 12
 
1.7%
Other values (103) 575
79.4%
ValueCountFrequency (%)
325 4
0.6%
329 5
0.7%
334 5
0.7%
335 8
1.1%
337 3
 
0.4%
340 3
 
0.4%
343 4
0.6%
344 8
1.1%
345 7
1.0%
348 7
1.0%
ValueCountFrequency (%)
1075 4
0.6%
883 4
0.6%
830 4
0.6%
712 5
0.7%
588 8
1.1%
573 4
0.6%
568 4
0.6%
566 4
0.6%
555 4
0.6%
554 5
0.7%

India_White_5%
Real number (ℝ)

Distinct78
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean383.47011
Minimum331
Maximum481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:55.423861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum331
5-th percentile345.63348
Q1345.63348
median375
Q3410
95-th percentile450
Maximum481
Range150
Interquartile range (IQR)64.366516

Descriptive statistics

Standard deviation36.384265
Coefficient of variation (CV)0.094881619
Kurtosis-0.58701129
Mean383.47011
Median Absolute Deviation (MAD)29.366516
Skewness0.66955692
Sum277632.36
Variance1323.8147
MonotonicityNot monotonic
2024-11-11T15:27:55.626542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
345.6334842 160
 
22.1%
350 24
 
3.3%
410 17
 
2.3%
370 17
 
2.3%
360 17
 
2.3%
373 17
 
2.3%
413 15
 
2.1%
375 15
 
2.1%
380 15
 
2.1%
398 15
 
2.1%
Other values (68) 412
56.9%
ValueCountFrequency (%)
331 4
 
0.6%
334 5
 
0.7%
340 4
 
0.6%
343 7
 
1.0%
345 4
 
0.6%
345.6334842 160
22.1%
347 4
 
0.6%
348 4
 
0.6%
350 24
 
3.3%
351 3
 
0.4%
ValueCountFrequency (%)
481 4
 
0.6%
475 4
 
0.6%
465 4
 
0.6%
464 10
1.4%
460 9
1.2%
457 4
 
0.6%
450 9
1.2%
445 4
 
0.6%
444 3
 
0.4%
443 7
1.0%

Pakistan_White_5%
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.14718
Minimum311
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.3 KiB
2024-11-11T15:27:55.815032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum311
5-th percentile345
Q1376.51128
median410
Q3443
95-th percentile513.95
Maximum850
Range539
Interquartile range (IQR)66.488722

Descriptive statistics

Standard deviation73.187879
Coefficient of variation (CV)0.17419581
Kurtosis13.152378
Mean420.14718
Median Absolute Deviation (MAD)33
Skewness2.9295124
Sum304186.56
Variance5356.4656
MonotonicityNot monotonic
2024-11-11T15:27:56.017571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
435 22
 
3.0%
367 21
 
2.9%
423 17
 
2.3%
393 16
 
2.2%
420 14
 
1.9%
475 14
 
1.9%
471 13
 
1.8%
444 13
 
1.8%
443 13
 
1.8%
410 13
 
1.8%
Other values (98) 568
78.5%
ValueCountFrequency (%)
311 4
0.6%
320 5
0.7%
323 4
0.6%
333 5
0.7%
334 4
0.6%
340 9
1.2%
343 4
0.6%
345 8
1.1%
346 3
 
0.4%
348 4
0.6%
ValueCountFrequency (%)
850 4
0.6%
838 4
0.6%
715 5
0.7%
670 4
0.6%
656 4
0.6%
555 4
0.6%
519 4
0.6%
516 4
0.6%
515 4
0.6%
508 4
0.6%

Interactions

2024-11-11T15:27:50.124274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:36.949957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:38.382276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:39.470526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.557300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:41.833629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:42.967811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:44.149559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:45.523493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:47.123366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:48.744359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:50.220988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:37.095386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:38.473628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:39.562240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.642028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:41.924188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:43.061197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:44.252062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:45.648152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:47.233265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:48.850200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:50.319806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:37.218780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:38.563267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:39.664391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.733000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:42.022466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:43.156989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:44.353301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:45.779013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:47.345955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:48.962969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:50.432490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:37.354097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:38.662264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:39.756078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.822989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:42.113547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:43.249182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:44.457724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:45.905183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:47.452942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:49.077899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:50.538258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:37.520785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:38.752533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:39.852926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.910514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:42.202998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:43.343198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:44.560719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:46.034339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:47.561963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:49.185460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:50.646370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:37.785773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:38.854001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:39.943269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.998116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:42.291716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:43.438965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:44.669567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:46.165483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:47.668479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:49.295305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:50.758789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:37.896523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:38.966340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.045055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:41.107307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:42.401035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:43.542636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:44.878196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:46.290651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:48.132825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:49.501006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:50.869578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:37.987928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:39.059071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.140416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:41.415413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:42.500158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:43.638202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:44.981420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:46.415973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:48.254610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:49.635754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:50.993214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:38.085682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:39.160286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.248035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:41.518186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:42.600327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:43.756438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:45.105632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:46.567805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:48.392167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:49.775309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:51.110815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:38.183764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:39.258359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.363962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:41.642703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:42.775454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:43.871522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:45.229405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:46.788806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:48.518241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:49.898111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:51.235750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:38.285414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:39.368076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:40.465608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:41.742035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:42.878783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:44.034281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:45.365819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:46.980695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:48.632869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-11T15:27:50.010085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-11T15:27:56.137925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Estimated PrecipitationEstimated TemperatureEx Rate (sight bill)India_White_5%Pakistan_White_5%Parboiled Rice 100%Thai Hom Mali Rice Grade BVietnam_White_5%White Broken Rice A.1 SuperWhite Glutinous Rice 10%White Rice 5%
Estimated Precipitation1.0000.4370.1050.0030.2050.0750.0150.0810.036-0.0590.038
Estimated Temperature0.4371.000-0.0280.0370.071-0.0330.026-0.0950.0100.104-0.048
Ex Rate (sight bill)0.105-0.0281.000-0.416-0.358-0.355-0.657-0.311-0.616-0.623-0.414
India_White_5%0.0030.037-0.4161.0000.169-0.0060.4030.0410.1890.3590.045
Pakistan_White_5%0.2050.071-0.3580.1691.0000.7970.2630.7280.5090.0080.785
Parboiled Rice 100%0.075-0.033-0.355-0.0060.7971.0000.3200.6310.5660.0140.980
Thai Hom Mali Rice Grade B0.0150.026-0.6570.4030.2630.3201.0000.1000.4040.4750.363
Vietnam_White_5%0.081-0.095-0.3110.0410.7280.6310.1001.0000.537-0.0940.652
White Broken Rice A.1 Super0.0360.010-0.6160.1890.5090.5660.4040.5371.0000.3140.638
White Glutinous Rice 10%-0.0590.104-0.6230.3590.0080.0140.475-0.0940.3141.0000.062
White Rice 5%0.038-0.048-0.4140.0450.7850.9800.3630.6520.6380.0621.000

Missing values

2024-11-11T15:27:51.414941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-11T15:27:51.634002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateEx Rate (sight bill)Thai Hom Mali Rice Grade BWhite Rice 5%White Broken Rice A.1 SuperWhite Glutinous Rice 10%Parboiled Rice 100%Estimated TemperatureEstimated PrecipitationVietnam_White_5%India_White_5%Pakistan_White_5%
02008-01-0933.05638.0374.0361.0558.0398.023.595159280.667674390.0345.633484390.0
12008-01-1633.00657.0377.0365.0529.0404.023.595159280.667674390.0345.633484390.0
22008-01-2332.94688.0387.0378.0514.0420.023.595159280.667674390.0345.633484390.0
32008-01-3032.86705.0418.0403.0510.0440.023.595159280.667674390.0345.633484390.0
42008-02-0532.73732.0444.0429.0518.0466.025.259548215.285593467.0345.633484478.0
52008-02-1332.70733.0445.0430.0518.0466.025.259548215.285593467.0345.633484478.0
62008-02-2032.32743.0457.0438.0537.0510.025.259548215.285593467.0345.633484478.0
72008-02-2732.02749.0470.0454.0542.0529.025.259548215.285593467.0345.633484478.0
82008-03-0531.41763.0495.0479.0552.0555.027.344929591.393121588.0345.633484555.0
92008-03-1231.36771.0544.0512.0553.0604.027.344929591.393121588.0345.633484555.0
DateEx Rate (sight bill)Thai Hom Mali Rice Grade BWhite Rice 5%White Broken Rice A.1 SuperWhite Glutinous Rice 10%Parboiled Rice 100%Estimated TemperatureEstimated PrecipitationVietnam_White_5%India_White_5%Pakistan_White_5%
7142022-10-1537.91873.0429.0382.0648.0434.026.5979932442.430153429.0380.0401.0
7152022-10-1937.92873.0429.0382.0675.0434.026.5979932442.430153429.0380.0401.0
7162022-10-2637.76876.0426.0383.0678.0431.026.5979932442.430153429.0380.0401.0
7172022-11-0237.39885.0427.0387.0684.0435.025.4909901125.129626438.0387.0432.0
7182022-11-0936.61904.0433.0395.0712.0444.025.4909901125.129626438.0387.0432.0
7192022-11-2335.92865.0442.0403.0698.0453.025.4909901125.129626438.0387.0432.0
7202022-11-3035.10856.0456.0412.0728.0464.025.4909901125.129626438.0387.0432.0
7212022-12-0734.80834.0460.0415.0735.0468.023.845445588.910824456.0388.0456.0
7222022-12-1434.36845.0465.0421.0744.0474.023.845445588.910824456.0388.0456.0
7232022-12-2134.54840.0477.0424.0740.0486.023.845445588.910824456.0388.0456.0